1. Field of the Invention
The present invention relates to IQ imbalance compensation, and more specifically, to a method and apparatus thereof for estimating and compensating the IQ imbalance at the base band in a receiver.
2. Description of the Related Art
Many wireless devices employ Radio Frequency (RF) direct-conversion as it has inherent advantages in cost, package size, and power consumption. The tradeoff is a higher degree of RF imperfection including In-phase (I) and Quadrature (Q) imbalance induced by the mismatch between the in-phase component and the quadrature component of the received signal. There are already several approaches widely used for IQ imbalance compensation, and two possible solutions are only included herein.
FIG. 1 is a block diagram showing a receiver compensating the IQ imbalance in Orthogonal Frequency Division Multiplex (OFDM) systems by estimating the IQ imbalance using the Least Mean Square (LMS) algorithm. This approach is disclosed in Consumer Electronics, IEEE Transactions, volume 47, issue 3, “A novel IQ imbalance compensation scheme for the reception of OFDM signals” The receiver, proposed by Schuchert A., Hasholzner R., and Antoine P. The receiver first receives the training sequence 11 from the channel, wherein IQ mismatch offset is induced as a result of the imperfection of the receiver. Blocks 111 and 121 model this IQ mismatch offset in the RF band with two parameters, α and β. The Fast Fourier Transform (FFT) 112 transforms the received signal from time domain to frequency domain, and sends it to an IQ estimator 113 for IQ estimation. Here, the parameters α and β14 of the IQ imbalance are estimated using the LMS algorithm according to a known local reference signal 13. The estimated parameters 14 are then passed to the IQ compensator 122 which compensates the data 12 after considering the effect of the IQ imbalance 121. The output of the IQ compensator 122 is then sent to the FFT 123. The IQ imbalance compensation scheme shown in FIG. 1, however, requires FFT computation as the LMS algorithm is operated in the frequency domain, resulting in significant power consumption and increased cost in the receiver. The performance of the IQ imbalance estimation depends on the accuracy of the sampling time, thus a strict local reference 13 must be provided to the IQ estimator 113. Furthermore, this approach does not consider the channel effect, Carrier Frequency Offset (CFO), and IQ imbalance induced in the transmitter.
FIG. 2 is a block diagram illustrating a receiver for IQ imbalance and frequency offset compensation in OFDM systems according to Acoustics, Speech, and Signal Processing, 2003, Proceedings (ICASSP' 03), 2003 IEEE International Conference on, Volume: 4, Apr. 6-10, 2003 “Frequency offset and I/Q imbalance compensation for OFDM direct-conversion receivers”, proposed by Guanbin Xing, Manyuan Shen, and Hui Liu. As shown in FIG. 2, the present scheme models both the CFO 211, 221 and IQ imbalance 212, 222 for the training sequence 21 and data 22 respectively. Upon receiving the training sequence 21, the CFO is estimated using a Nonlinear Least Square (NLS) algorithm in block 213, then the IQ imbalance is estimated using a Least Square (LS) algorithm in block 214. Then, the training sequence 21 is passed to block 215 and 216 for FFT computation and equalization (EQ). The estimated CFO parameters are passed to the CFO compensator 224, and similarly, the estimated IQ imbalance parameters are passed to the IQ compensator 223. Therefore the IQ mismatch offset and the CFO in the data received by the receiver can be thoroughly removed. The compensated data is then performed FFT computation and Equalization (EQ) in block 225 and 226 respectively. Herein, the parameters used in the EQ block 226 are derived from the EQ estimator 216. The drawback of this approach is that complicated computations are required for precise CFO estimation. The IQ estimator 214 is unable to provide adequate IQ compensation if the CFO estimator 213 has a poor performance.